%matplotlib inline
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
E:\Anaconda3\envs\TIL6022\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
from matplotlib.pyplot import MultipleLocator
x = stocks['date']
y = stocks['GOOG']
fig, ax = plt.subplots()
ax.plot(x,y)
x_major_locator = MultipleLocator(14)
ax.xaxis.set_major_locator(x_major_locator)
plt.rcParams["figure.figsize"] = (25, 10)
ax.set_title('Google Stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
plt.show()
# YOUR CODE HERE
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
from matplotlib.pyplot import MultipleLocator
x = stocks['date']
y1 = stocks['GOOG']
y2 = stocks['AAPL']
y3 = stocks['AMZN']
y4 = stocks['FB']
y5 = stocks['NFLX']
y6 = stocks['MSFT']
fig, ax = plt.subplots()
ax.plot(x,y1,label = 'GOOG')
ax.plot(x,y2,label = 'AAPL')
ax.plot(x,y3,label = 'AMZN')
ax.plot(x,y4,label = 'FB')
ax.plot(x,y5,label = 'NFLX')
ax.plot(x,y6,label = 'MSFT')
x_major_locator = MultipleLocator(14)
ax.xaxis.set_major_locator(x_major_locator)
plt.rcParams["figure.figsize"] = (20, 10)
ax.set_title('Stocks')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
plt.legend()
plt.show()
# YOUR CODE HERE
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
# YOUR CODE HERE
# Question: Are there differences between Lunch and Dinner when it comes to giving tips?
g = sns.FacetGrid(tips, col='time', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.savefig('smoker.png', dpi=200)
plt.show()
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE
df = px.data.stocks()
fig = px.line(df, x="date", y=["GOOG",'AAPL','AMZN','FB','NFLX','MSFT'])
fig.show()
# YOUR CODE HERE
# Question: Are there differences between Lunch and Dinner when it comes to giving tips?
fig = px.scatter(tips, # 数据
x="total_bill", # xy轴
y="tip",
color="smoker", # 颜色
facet_col="time" # 列方向切面字段
)
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# YOUR CODE HERE
df_2007 = df.query('year == 2007')
df_sum = df_2007.groupby('continent').sum()
fig = px.bar(df_sum, x = "pop",y = df_sum.index,orientation = 'h',color = df_sum.index,text = 'pop')
fig.update_yaxes(categoryorder = "sum ascending")
fig.show()